Context-sensitive identification of regions of interest in a medical image

ABSTRACT

A voice controlled system uses context-sensitive interpretation of voice comments received by a voice recognition system to identify a region of patient image data identified by a verbal comment.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method, system and device forcontext-sensitive identification of regions of interest in a medicalimage.

2. Description of the Prior Art

Medical imaging processes such as PET provide functional image datarepresenting the functioning of a patient's body, while imagingprocesses such as MRI or x-ray CT provide anatomical image data,representing the patient's anatomy. It is common to evaluatecorresponding anatomical and functional data together, to matchfunctional information with anatomical localisation.

Clinical assessment of an oncological PET functional scan begins with aclinician performing a visual assessment of radiotracer distribution inorder to identify abnormal patterns of uptake which may be indicative ofmalignant lesions or other pathologies.

This clinical review (“read”) of functional PET data by a trainedclinician is typically supported by an assessment of co-acquiredanatomical CT data. The combined PET/CT assessment is a sophisticatedprocess requiring a lot of concentration by the clinician and aresponsive and flexible software application to support the read withminimal interruptions to the flow of concentration of the clinician.

Once the clinician has formed a clear understanding of the patientcondition from the image data, their findings must be documented in areport for a referring physician to inform the treatment decision.Creation of such documentation is typically a cumbersome task that doesnot in itself require expert clinical reasoning. Expert clinicalreasoning is required for the assessment, but the report-writing ratherrequires an ability to describe synthetically and clearly the locationand classification of any abnormalities along with the clinicalimpression based on these findings.

During the interpretation of the patient image data, the act of makingmeasurements interrupts the clinician during the clinical read, due tothe need to manipulate software and perform the relevant measurements.

The case is then revisited for the reporting of the findings, typicallyvia a dictated report.

The format of such reports varies considerably from one institution toanother, despite communal efforts towards standardisation. A common stepin the creation of the report is the description of the clinicalfindings such as perceived abnormalities. It would be useful to providea standard report format for consistency and to aid the referringphysician who may receive such reports from several sources.

In a typical workflow, the clinician first identifies features withinthe patient image data which appear to represent lesions. Appropriatemeasurements are typically then performed on the image features, such aslesion size; tracer uptake. The clinician then reviews the findings todescribe the case and uses a dictation system to transcribe thisdescription into a report. The clinician will also re-read the reportonce it is created. The report may be created by another person who hasnot read the case and only formats the report for the clinician.

Such multiple reviews of a single patient data set are inefficient.There is also the possibility of errors in the transcription, such as alesion reported with an incorrect measurement value; a lesion identifiedto the wrong organ, etc.

SUMMARY OF THE INVENTION

An object of the present invention is to simplify the reviewing andreporting process by automatically compiling the relevant informationneeded for reporting as the clinician reads the case. This is achievedusing a context-sensitive method for identifying features in patientimage data based on a description provided by a reading clinician.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a patient anatomical image data set.

FIG. 2 shows a corresponding example patient functional image data set.

FIG. 3 shows a surface rendering of segmented organs as represented inFIG. 1.

FIGS. 4 and 5 show axial slices of patient functional image dataannotated with automatically generated labels and measurements.

FIG. 6 shows steps of the method according to an embodiment of thepresent invention.

FIG. 7 is a block diagram of a system according to an embodiment of thepresent invention.

FIG. 8 schematically illustrates an apparatus according to an embodimentof the invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention employs a combination of automatic determinationof possible descriptions for possible regions of interest represented inpatient functional data using an awareness of which possible regions ofinterest are currently rendered to the screen in a display visible tothe clinician, and then selecting from this list, the possible region ofinterest that most closely matches the verbal description given by theclinician, given the context of the displayed data viewed by theclinician. Uncertainties are introduced by each of the components. Forexample, the conversion of voice to text is error prone; automaticlabelling of the possible regions of interest may identify a range ofpossible labels with different ‘likelihoods’ for any one particularregion. The optimal performance of the method and system of the presentinvention will require more than a simple combination of thesecomponents. A consideration of the inherent uncertainties from eachcomponent in a context-sensitive manner provides a determination of themost likely solution.

In some cases, the method of the present invention does not identify asingle most likely feature, but provides approaches for handling asituation when a single region cannot be confidently selected.

In presently-preferred embodiments, the method of the present inventionemploys a voice controlled system to support the identification offeatures and/or the taking of measurements during the clinician'sinterpretation (“read”) of the patient image data.

FIG. 1 represents an example patient anatomical image data set 10. Inparticular, FIG. 1 shows a typical x-ray computed tomography (CT)patient anatomical data set with automatically-segmented organs, andanatomical areas shown by dashed lines as computed from anatomicallandmarks detected from the CT image.

FIG. 2 represents a corresponding example patient functional image dataset 20. In particular, FIG. 2 shows a typical PET-CT patient image datafor a patient with a primary tumour in the right lung and ipsilaterallymph node metastases. In FIG. 2, dark regions 22, 24 identified witharrows resemble representations of a primary tumour in the right lung,in the case of region 24, and ipsilateral lymph node metastases, in thecase of region 22.

In a method of the present invention, the anatomical data set 10 issegmented, using a method known in itself to those skilled in the art,to define three-dimensional regions within the anatomical data whichcorrespond to known organs.

FIG. 3 represents surface rendering of various segmentations of theanatomical image data set 10. Representations of various organs in thethorax, abdomen and pelvis regions of the patient image data set areidentified. As is known in the art, each segmentation may have a labelassociated with it, identifying the corresponding organ.

As is conventional, a clinician reads a set of patient image data setfrom top to bottom, that is, starting at the head end. Typically, theclinician uses a coronal projection view such as shown in FIGS. 1 and 2to select axial slices which represent image data in a planeperpendicular to the plane of FIGS. 1 and 2. Examples of such axialslices of functional data are represented in FIGS. 4 and 5.

Conventionally, the reviewing clinician navigates through the axialslices using a computer mouse or similar physical-contact data inputdevice to select relevant areas of interest. Such a method may be usedin the present invention, or a voice recognition system may be employedto perform a similar process of navigation through axial slices.Alternative methods for navigating the patient image data set may ofcourse be employed.

The system of the present invention includes a segmentation device whichsegments features within the functional image data into possible volumesof interest.

When the clinician reviews the patient image data set, a dictationdevice, which may be embodied in software within the voice controlledsystem, may be used in embodiments of the present invention to commenton any findings that they regard as worthy of inclusion in the report,for instance, “there is a suspicious lesion in the right lung”.

According to a feature of the present invention, this comment isreceived by a voice recognition subsystem, which may be embodied insoftware within the voice controlled system. The voice controlled systeminterprets the received comment and automatically identifies themost-likely segmented feature the clinician is referring to from thereceived voice description using a context-sensitive method which willbe discussed in further detail below.

According to preferred embodiments of the present invention, thisautomatic identification of the most-likely segmented feature isperformed in a context-sensitive manner using a combination ofanatomical knowledge, for example derived from conventional organsegmentation of the underlying MR or CT image data 10, 20 and anassessment of the local voxel intensity distribution in thecorresponding segmentation of the functional and/or anatomical imagedata sets to provide possible volumes of interest.

As an alternative to the above-mentioned organ segmentation method, therequired anatomical knowledge may be obtained from automaticallydetected anatomical landmarks. The reading clinician may then identify apossible volume of interest by reference to its proximity to suchanatomical landmarks in a viewed rendering of the patient image data.

The assessment of local voxel intensities enables the system to identifylikely lesions within the patient image data, and these likely lesionsare segmented into possible volumes of interest which are then matchedby the system to the clinician's comments.

For example, using the patient data represented in FIGS. 1 and 2, andthe clinician's comment “there is a suspicious lesion in the rightlung”, segmentation of the anatomical data of FIG. 1 identifies the“right lung”. The voice controlled system of the present inventionrecognises the label “right lung”, and limits further consideration ofpatient data to the patient data within the region identified as “rightlung”. This is an element of a context-aware method of the presentinvention.

Within the organ segmentation labelled “right lung”, two segmentedregions 22, 24 of high voxel data values are identified—shown by arrowsin FIG. 2. The clinician's comment “lesion in right lung” may beinterpreted by the voice controlled system to indicate one of those tworegions. The remainder of the comment “there is a suspicious . . . ” maybe interpreted by the system as superfluous, and discarded. In certainembodiments, however, adjectives such as “suspicious”; “possible”;“likely” may be used by the system to identify regions of high voxeldata values of a suitable clarity, size and/or intensity represented bysuch description.

Similarly, adjectives such as “large”, “small”, “round”, “elongate” maybe used by the clinician and interpreted by the system to identify themost likely region as the subject of the clinician's comments.

The voice controlled system may then further interpret the clinician'scomment to determine which of these two segmented regions 22, 24 of highvoxel data values the clinician is referring to.

FIG. 4 shows an axial slice 40 of the functional patient data set 20,corresponding to a position in the lungs. This slice passes throughregion 24 of high voxel data values also represented in FIG. 2. In thisexample, the clinician is viewing this axial slice 40 when making thecomment “there is a suspicious lesion in the right lung”.

The system could use the constraint that the region of high voxel datavalues the clinician refers to is visible in at least one of thecoronal, axial or sagittal slices displayed at the time the clinicianprovides the description. This awareness of which regions 22, 24 arepresently viewed by the clinician provides an element of acontext-sensitive method of the present invention.

In the present example, as only one of the identified regions 22, 24 ofhigh voxel data values is visible in the axial slice 40 currently beingviewed, the voice controlled system of the present invention makes acontext-sensitive interpretation that the region of high voxel datavalues referred to by the clinician is the one 24 shown in thepresently-viewed slice 40, as the other identified possibility, region22, is not visible in the viewed axial slice. This may be the case evenif both regions of high voxel data values are shown in coronal, orsagittal slices displayed at the time.

The present invention accordingly provides a voice controlled systemwhich used context-sensitive interpretation of voice comments receivedby a voice recognition system to identify a region of patient image dataidentified by a verbal comment.

The system may proceed to automatically compute and display relevantmeasurements that are associated with the identified region. The typesof measurement which are performed may be preselected by the clinicianaccording to personal preference, or as determined by the type of maladysought by the reading process. Alternatively, or in addition, the systemmay be arranged to perform a default set of measurements in the absenceof preselected measurement types.

In the example shown in FIG. 4, the region identified by thecontext-sensitive interpretation of voice comments received by a voicerecognition system is region 24 and has been automatically labelled“Right_Lung_VOI_1” by the system. The label “Right_Lung” may be thelabel of the corresponding organ segmentation, or image region definedby spatial relationship to anatomical markers. Alternatively, it may bederived from the voice recognition of the comment. The remainder of thelabel “_VOI_1” may be used to indicate that this region is the firstvolume of interest to be identified within the right lung. Otherlabelling conventions may of course be used with similar effect.

Three measurements are also shown, along with the label, in a boxassociated with the region 24 of high voxel data values identified bythe system. In this example, the measurements are: SUVmax (the value ofthe voxel with the highest SUV in the segmented lesion VOI); SUVpeak(the mean value from a 1 cm³ sphere positioned within the segmentedlesion VOI so as to maximise this mean value); Metabolic Tumour Volume(MTV) (the spatial volume of the segmented lesion VOI 24).

The system may be set up to automatically calculate these three valuesfor each identified region of high voxel data values, or they may havebeen specifically requested by the clinician.

For example, when reviewing the patient image data, the clinician couldsay the command “RECIST measurement for the right lung lesion”. Thevoice controlled system would recognise “the right lung lesion” as thealready-identified region of high voxel data values 24. The voicerecognition system would also recognise “RECIST measurement” as one of aset of predefined measurement algorithms available to the voicecontrolled system. A corresponding computation of the RECIST measurementin the part of the CT image 10 corresponding to segmented region 24 anddisplay of the information on the screen would be performed in responseto this recognition, without the clinician having to perform anyphysical-contact data entry, such as computer mouse operation. Suchphysical-contact data entry is believed to distract the clinician'sconcentration on interpreting the presented patient image data, and sois avoided according to a feature of certain embodiments of the presentinvention.

In addition, or alternatively, to the default or pre-selectedmeasurements, the reading clinician may request calculation ofappropriate measurements once a volume of interest has been identifiedby the system according to the present invention. For example, theclinician could say “SUV max, peak and metabolic volume from 50% max”,which would trigger the system to compute these quantities automaticallyfrom the PET data and display them on the screen.

In some instances, there may be ambiguity in the region selection, forinstance, when multiple regions of high voxel data values are visible tothe clinician and the information recorded by the dictation system andvoice recognition system does not help disambiguate multiple possiblecandidates. In other instances, errors may occur such that the regionselected by the system is not the one intended by the clinician. In suchcases, the clinician could either confirm which one is to be reportedusing a computer mouse or similar physical-contact input device, or theclinician could provide further spoken information such as “the onecloser to the heart”, “not that one”, “the most superior lesion”, orstrategic hints like “all lymph nodes in station 7 as a group”, etc.until the clinician is satisfied with the choice of region made by thesystem, representing the appropriate volume of interest in 3D patientimage data.

Lymph nodes in the mediastinum are conventionally grouped into spatiallydistinct regions, called “stations”. These stations are defined based onnearby anatomical features and are numbered. Station 7 is one of theseregions.

Alternatively, where the system has been unable to distinguish theuser's intention with certainty among a plurality of possible regions ofinterest, the system could label each identified possible region ofinterest with a different label such as “A”, “B”, “C”, etc., and theclinician could select the required region by selecting thecorresponding label, for example by voice or by using a physical-contactinput device such as a computer mouse.

The invention is further illustrated by further reference to the patientimage data represented in FIG. 2, which shows a MIP of a typical lungcancer PET scan with a primary tumour in the right lung and ipsilaterallymph node lesions. The MIP represents the image with the maximumintensities along the line of sight, and is an effective way tosummarise a case where areas of high uptake are typically worthy ofinvestigation.

The reading clinician is likely to be reading slice 44 shown in FIG. 5when identifying the suspected ipsilateral lymph node. The clinician mayrespond to this findings by saying “ipsilateral lymph node”. The systemidentifies region 22 as being the most probable region as the subject ofthe clinician's comment.

The system may name this region according to its position within anorgan segmentation and/or the description given by the readingclinician.

In this example, shown in FIG. 5, the name allocated is“Right_Med_LN_stn12_1”, indicating RIGHT MEDiastinal Lymph Nodeidentified by STatioN 12, and the first volume of interest identified bythese labels. As discussed with reference to FIG. 4, certainmeasurements and calculations may be made, in this example SUVmax,SUVpeak and Metabolic Tumour Volume (MTV).

Should the reading clinician disagree with the allocated label, it couldbe corrected either by voice instructions or by a physical-contact datainput device such as a computer mouse. For example, the readingclinician can correct the system by providing a partial new name:“incorrect station: station 6”. This comment would be received andinterpreted by the system, which would react by changing the name of theregion to “Right_Med_LN_stn6_1”.

Alternatively, or in addition, the system may propose a selection ofpossible names for the identified region, and the reading cliniciancould choose from among them using a physical-contact data entry devicesuch as a computer mouse or by voice commands.

The system may propose a drop-down menu of suggested possibilities, forexample:

-   -   “Right_Med_LN_stn12_1”    -   “Right_Med_LN_stn6_1”    -   “Other: Please specify”.

The system may arrange these alternatives alphabetically or indescending order of a calculated probability of correctness.

In alternative embodiments, the naming of a region of interest may bedetermined by comparing location, shape, and SUV measurements of theregion of interest, along with distribution of regions of interest inpatient, to a database of records of previously-labelled regions ofinterest. A similarity index may be calculated by comparing featuressuch as anatomical location, PET and CT data intensity distribution,lesion size of the region of interest to be named, along withdistribution of regions of interest in patient, with the records in thedatabase, and the record, or records, having the highest similarityindex providing an example name upon which a name for the new region ofinterest may be based.

According to aspects of the present invention, the describedidentification of possible volumes of interest by matching features ofthe image data with descriptions provided by the reading clinician maybe used to trigger delineation and other measurements, as describedabove, and may also be used to associate the identified volume ofinterest with other functions of system software, for instance,generation of snapshots, creation of material for structured reporting,annotation mark-up for the image for supporting follow-up reads orretrospective analysis, etc.

Typically, the identified regions of interest with their associatedmeasurements, description and further comments made by the readingclinician will be collated and stored in a report format for provisionto a referring physician. The reading clinician may be given anopportunity to revise the report, but it will not require the fullreview conventionally required, as the clinician will have generated thecontent of the report during the read, and the report will be presentedby the system of the present invention in a standard format, makinginterpretation of multiple reports from multiple sources much simplerfor the referring physician.

According to the methods and systems of the present invention, there isprovided automatic delineation and measurements of possible regions ofinterest in a patient medical image data set. The patient medical imagedata set typically comprises both anatomical and functional data.

Methods of the present invention may involve

-   -   preprocessing, such as segmentation and anatomical parsing, of        anatomical data associated with the functional data;    -   identifying possible regions of interest within the functional        data;    -   parsing and recognition of voice input specifying a region of        interest to be segmented;    -   interpretation of the voice input to select the most likely of        the identified possible regions of interest to correspond to the        voice input according to an anatomical or clinical description        within the voice input;    -   calculation of measurements related to the selected region of        interest.

FIG. 6 shows steps in an example method according to an embodiment ofthe present invention.

In the illustrated method, step 102 recites providing anatomical patientdata set and provides patient functional data set. Step 104 recitesdefining three-dimensional regions within the anatomical data set whichcorrespond to known organs. Step 106 recites rendering and displayingthe patient functional image data set: optionally combined with thepatient anatomical data set. Step 108 recites assessing localvoxel-intensity distribution to segment features corresponding topossible volumes of interest. Step 110 recites receiving comments fromclinician and interpreting those comments. Step 112 recites combiningthose comments with an awareness of currently-displayed patient data todetermine a likely feature corresponding to a region of interestidentified by the clinician. Step 114 recites automatically generating alabel for the volume of interest. Step 116 recites performingmeasurements on the volume of interest. Step 118 recites displaying thelabel and results of the measurements to the clinician. Step 120 recitesstoring the label, segmentation of the region of interest and themeasurements in a report.

FIG. 7 shows a block diagram of a system 200 according to an embodimentof the present invention.

A conventional medical imaging device 190 acquires and providesanatomical and functional patient image data 192 to a data store 194. Aninput device 196 operated by a user provides patient data recordselection signals 198 to the data store to select a patient data recordfor study. In response, a selected patient image data record 199 isprovided to the system 200 of the present invention.

The selected patient image data record 199 is received by a voicecontrolled system 202. The patient data record is treated by regimentingof possible regions of interest 204, typically by identifying regions ofhigh local voxel data values. The data record and the segmentation ofpossible regions of interest are provided to a processing unit. The datais rendered and displayed on a display 210 for viewing by the readingclinician. The reading clinician reviews the displayed data, andresponds by providing voice commands and comments to a voice inputsubsystem or a physical-contact input device 214, such as a computermouse. The clinician's activity in viewing data records and providingcommands and comments effectively provides a feedback loop 212 shown bya dashed line in FIG. 7.

Voice input signals are transmitted from voice input subsystem 213 tovoice recognition subsystem 216 within voice controlled system 200. Thevoice input comments and comments are recognised and interpreted into aform suitable for provision to processing unit 206.

The processing unit 206 acts on the patient data record 199 according toinput commands and comments provided by the reading clinician asdescribed above. As the reading clinician completes each stage of thereview, or at the end of the review if preferred, data 218 representingthe clinician's findings and comments are provided to a report stage,including an arrangement 220 for report formatting, which produces as anoutput a report 222 to the referring physician. The reading clinicianmay review this report before it is sent.

The system of the present invention may be at least partiallyimplemented in software within a general-purpose computer. FIG. 8illustrates such an embodiment of the present invention.

For example, a central processing unit 4 is able to receive datarepresentative of medical scan data via a port 5 which could be a readerfor portable data storage media (e.g. CD-ROM); a direct link withapparatus such as a medical scanner (not shown) or a connection to anetwork.

For example, in an embodiment, the processor performs such steps asautomatically identifying and segmenting possible regions of interestwithin functional patient image data, displaying the functional patientimage data to a user in one or more views, receiving comments from theuser regarding a region of interest, evaluating, in the context of theviews presented to the user, and the comments, which of the segmentedpossible regions of interest is most likely to be the subject of theuser's comments; and displaying a representation of that region ofinterest to the user.

Software applications loaded on memory 6 are executed to process theimage data in random access memory 7.

A Man—Machine interface 8 typically includes a keyboard/mouse/screencombination (which allows user input such as initiation of applications)and a screen on which the results of executing the applications aredisplayed.

Although modifications and changes may be suggested by those skilled inthe art, it is the intention of the inventors to embody within thepatent warranted hereon all changes and modifications as reasonably andproperly come within the scope of their contribution to the art.

We claim as our invention:
 1. A method for identifying a region ofinterest within patient functional image data, comprising: providingpatient functional image data to a computer and, in said computer,automatically identifying and segmenting candidate regions of interestswithin the patient functional image data; displaying the patientfunctional image data at a user interface of the computer, in at leastone view; receiving verbal comments, that describe a region of interest,into the computer from a user via a voice input interface of thecomputer and, in said computer, automatically interpreting said commentsby executing a voice recognition algorithm in the computer; in saidcomputer, automatically evaluating said interpreted verbal comments,dependent on said at least one view, to identify a region of interest,among said candidate regions of interest, that most closely correspondsto the description of the region of interest in the interpreted verbalcomments; and displaying said most closely corresponding region ofinterest at said user interface.
 2. A method as claimed in claim 1comprising, receiving, as said verbal comments, respective descriptionsof a plurality of regions of interest and evaluating each verbaldescription to identify a region of interest, among said candidateregions of interests, that most closely corresponds thereto, andallowing further interaction with said computer by said user to selectone region of interest among said most closely corresponding regions ofinterest.
 3. A method as claimed in claim 2 comprising implementing saidfurther interaction verbally, with a further voice input via said voiceinput interface and by interpreting said further voice input with saidvoice recognition algorithm.
 4. A method as claimed in claim 1comprising, in said computer, automatically generating a name for saidmost closely corresponding region of interest.
 5. A method as claimed inclaim 4 comprising automatically generating said name by identifying ananatomical location of said most closely corresponding region ofinterest, and dependent on a description thereof.
 6. A method as claimedin claim 5 comprising obtaining said description from said verbalcomments.
 7. A method as claimed in claim 5 comprising obtaining saiddescription by a comparison of said most closely corresponding region ofinterest with a database of records of previously-labeled regions ofinterest.
 8. A method as claimed in claim 1 comprising, in saidcomputer, automatically performing a measurement of said most closelycorresponding region of interest according to a predetermined list ofmeasurements to be performed, which is accessed by said computer.
 9. Amethod as claimed in claim 1 comprising performing a measurement of saidmost closely corresponding region of interest in said computer,according to an instruction entered into said computer by the user. 10.A method as claimed in claim 9 comprising entering said instructionverbally as a further voice input via said voice input interface, andinterpreting said instruction with said voice recognition algorithm. 11.A method as claimed in claim 1 comprising formatting data concerningsaid most closely corresponding region of interest, and providing theformatted data into a report in said computer, and making said reportavailable in electronic form from said computer.
 12. A system forcontext-sensitive identification of regions of interest, comprising: acomputer provided with patient functional image data to a computer, saidcomputer being configured to automatically identify and segmentcandidate regions of interests within the patient functional image data;a display monitor in communication with said computer, being configuredto display the patient functional image data at said display monitor auser interface of the computer, in at least one view; said computercomprising a voice input interface, and said computer being configuredto receive verbal comments, that describe a region of interest, via saidvoice input interface, and computer being configured to automaticallyinterpret said comments by executing a voice recognition algorithm inthe computer; said computer being configured to automatically evaluatesaid interpreted verbal comments, dependent on said at least one view,to identify a region of interest, among said candidate regions ofinterest, that most closely corresponds to the description of the regionof interest in the interpreted verbal comments; and said computer beingconfigured to display said most closely corresponding region of interestat said display monitor.
 13. A system as claimed in claim 12 whereinsaid computer is configured to execute a report formatting algorithm togenerate a report concerning said most closely corresponding region ofinterest, and to make said report available in electronic form from saidcomputer.
 14. An apparatus for identifying a region of interest inmedical imaging data of a subject, comprising: a computer provided withpatient functional image data, said computer being configured toautomatically identify and segment candidate regions of interests withinthe patient functional image data; a display monitor in communicationwith said computer, said computer being configured to display thepatient functional image data at said display monitor, in at least oneview; a user interface in communication with said computer, saidcomputer being configured to receive a description of a region ofinterest from a user via said user interface; said computer beingconfigured to automatically evaluate said description dependent on saidat least one view, to identify a region of interest, among saidcandidate regions of interest, that most closely corresponds to thedescription of the region of interest in the description; and saidcomputer being configured to display said most closely correspondingregion of interest at said display monitor.
 15. A non-transitory,computer-readable data storage medium encoded with programminginstructions, said storage medium being loaded into a computer and saidprogramming instructions causing said computer to: receive patientfunctional image data and automatically identify and segment candidateregions of interests within the patient functional image data; displaythe patient functional image data at a user interface of the computer,in at least one view; receive verbal comments, that describe a region ofinterest, from a user via a voice input interface of the computer, andautomatically interpret said comments by executing a voice recognitionalgorithm; automatically evaluate said interpreted verbal comments,dependent on said at least one view, to identify a region of interest,among said candidate regions of interest, that most closely correspondsto the description of the region of interest in the interpreted verbalcomments; and display said most closely corresponding region of interestat said user interface.
 16. A non-transitory, computer-readable datastorage medium encoded with programming instructions, said storagemedium being loaded into a computer and said programming instructionscausing said computer to: receive patient functional image data andautomatically identify and segment candidate regions of interests withinthe patient functional image data; display the patient functional imagedata at a display screen of the computer, in at least one view; receivea description of a region of interest from a user interface of thecomputer; automatically evaluate said description, dependent on said atleast one view, to identify a region of interest, among said candidateregions of interest, that most closely corresponds to the description ofthe region of interest; and display said most closely correspondingregion of interest at said display screen.